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1.
Ann Med ; 54(1): 3146-3156, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2097042

ABSTRACT

BACKGROUND: Role models are essential in medical education, yet empirical research is relatively insufficient on the influence of prosocial modelling on medical students' career commitment. The prosocial behaviour of medical staff involved in the fight against the novel coronavirus disease 2019 (COVID-19) at the beginning of 2020 presents an opportunity to fill the research gap. We explored and compared the different associations of the two most important role models for medical students - parents and faculty- with medical students' career commitment. METHODS: The cross-sectional study was conducted with 99,559 undergraduate students majoring in clinical medicine in mainland China. Questions were asked to collect information about participants in the battle against COVID-19, medical students' determination to practice medicine after graduation, as well as students' socio-demographic characteristics. Chi-square tests and hierarchical regressions were performed to examine the associations between parent and faculty involvement and students' career commitment. RESULTS: The results showed statistically significant associations between prosocial modelling during the COVID-19 pandemic in China and students' intention to pursue medical careers. The association of faculty involvement (OR = 1.165, p < .001) with students' career commitment was greater than that of parents (OR = 0.970, p > .05). For faculty involvement, the association was stronger among male students (OR = 1.323, p < .001) and students who were already determined to be doctors (OR = 1.219, p < .001) before the pandemic. CONCLUSIONS: Our study provides new evidence on the potential roles of parents and faculty in shaping medical students' career commitment. Encouraging faculty to act as positive role models could help medical students increase their intention to become doctors.KEY MESSAGESProsocial modelling could enhance students' intention to pursue medical careers.The association of prosocial behaviour of faculty is larger than that of parents on medical students.Those who have prior medical career commitment are much more likely to persist in the medical profession, and prosocial modelling of faculty is positively associated with their medical career commitment.


Subject(s)
COVID-19 , Students, Medical , Male , Humans , COVID-19/epidemiology , Career Choice , Cross-Sectional Studies , Pandemics , Faculty , Parents
2.
J Theor Biol ; : 111337, 2022 Nov 05.
Article in English | MEDLINE | ID: covidwho-2095715

ABSTRACT

During the SARS-CoV2 pandemic, epidemic models have been central to policy-making. Public health responses have been shaped by model-based projections and inferences, especially related to the impact of various non-pharmaceutical interventions. Accompanying this has been increased scrutiny over model performance, model assumptions, and the way that uncertainty is incorporated and presented. Here we consider a population-level model, focusing on how distributions representing host infectiousness and the infection-to-death times are modelled, and particularly on the impact of inferred epidemic characteristics if these distributions are misspecified. We introduce an SIR-type model with the infected population structured by 'infected age', i.e. the number of days since first being infected, a formulation that enables distributions to be incorporated that are consistent with clinical data. We show that inference based on simpler models without infected age, which implicitly misspecify these distributions, leads to substantial errors in inferred quantities relevant to policy-making, such as the reproduction number and the impact of interventions. We consider uncertainty quantification via a Bayesian approach, implementing this for both synthetic and real data focusing on UK data in the period 15 Feb-14 Jul 2020, and emphasising circumstances where it is misleading to neglect uncertainty. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

3.
J Clean Prod ; : 134784, 2022 Nov 04.
Article in English | MEDLINE | ID: covidwho-2095588

ABSTRACT

Most supply chain and production systems faced multiple manufacturing and delivery challenges during COVID-19 and transformed their supply chain for improved customer service. These challenges are mostly related to stocking and managing the inventory flow throughout the supply chain (from manufacturer to end consumer). Due to the COVID-19 travel and movement restrictions, inventory reorganisation is necessary for fulfilling consumer demand with adequate service facilities. The safety and serviceability of inventory consumption is the primary concern of many retail grocery stores and consumers. Maintaining the supply of groceries items during and post COVID-19 time without disruption is a real operational and policy challenge. Therefore, this research tries to solve an inventory pricing mechanism and retailer's profit under the optimal service level and retailers' promotional efforts. The proposed optimisation model is validated in the grocery retail sector. The grocery retail market situation is modelled when the demand for the grocery product (which may be essential items) and selling price depending on the investment in item promotional effort and consumer serviceability. The retail grocery store's investment in the product promotional efforts, such as awareness of the item availability and no-contact delivery which, may attract consumers. Therefore, the proposed inventory consumption is modelled with an optimisation problem to maximise the store profit with the optimal investment in promotional activities and service facilities to the consumers and maintain an optimal replenishment cycle. The optimisation model is tested with three different cases (no investment in promotional efforts, no investment in service facility, and investment in both) of investment to maximise the retailer's profit and stock availability. The optimality results depicted that investment in promotional efforts and service facility givens higher profit to the retailer. The proposed optimisation model's policy implications would help grocery retail store managers to develop operational strategies for maximising profit with the optimal service level and promotional efforts.

4.
Artif Life ; : 1-24, 2022 Oct 21.
Article in English | MEDLINE | ID: covidwho-2089008

ABSTRACT

Since the beginning of the COVID-19 pandemic, various models of virus spread have been proposed. While most of these models focused on the replication of the interaction processes through which the virus is passed on from infected agents to susceptible ones, less effort has been devoted to the process through which agents modify their behaviour as they adapt to the risks posed by the pandemic. Understanding the way agents respond to COVID-19 spread is important, as this behavioural response affects the dynamics of virus spread by modifying interaction patterns. In this article, we present an agent-based model that includes a behavioural module determining agent testing and isolation propensity in order to understand the role of various behavioural parameters in the spread of COVID-19.

5.
R Soc Open Sci ; 9(10): 220021, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-2087952

ABSTRACT

Coronavirus disease 2019 (COVID-19) forecasts from over 100 models are readily available. However, little published information exists regarding the performance of their uncertainty estimates (i.e. probabilistic performance). To evaluate their probabilistic performance, we employ the classical model (CM), an established method typically used to validate expert opinion. In this analysis, we assess both the predictive and probabilistic performance of COVID-19 forecasting models during 2021. We also compare the performance of aggregated forecasts (i.e. ensembles) based on equal and CM performance-based weights to an established ensemble from the Centers for Disease Control and Prevention (CDC). Our analysis of forecasts of COVID-19 mortality from 22 individual models and three ensembles across 49 states indicates that-(i) good predictive performance does not imply good probabilistic performance, and vice versa; (ii) models often provide tight but inaccurate uncertainty estimates; (iii) most models perform worse than a naive baseline model; (iv) both the CDC and CM performance-weighted ensembles perform well; but (v) while the CDC ensemble was more informative, the CM ensemble was more statistically accurate across states. This study presents a worthwhile method for appropriately assessing the performance of probabilistic forecasts and can potentially improve both public health decision-making and COVID-19 modelling.

6.
J Theor Biol ; 557: 111332, 2022 Oct 30.
Article in English | MEDLINE | ID: covidwho-2086501

ABSTRACT

In March 2020 mathematics became a key part of the scientific advice to the UK government on the pandemic response to COVID-19. Mathematical and statistical modelling provided critical information on the spread of the virus and the potential impact of different interventions. The unprecedented scale of the challenge led the epidemiological modelling community in the UK to be pushed to its limits. At the same time, mathematical modellers across the country were keen to use their knowledge and skills to support the COVID-19 modelling effort. However, this sudden great interest in epidemiological modelling needed to be coordinated to provide much-needed support, and to limit the burden on epidemiological modellers already very stretched for time. In this paper we describe three initiatives set up in the UK in spring 2020 to coordinate the mathematical sciences research community in supporting mathematical modelling of COVID-19. Each initiative had different primary aims and worked to maximise synergies between the various projects. We reflect on the lessons learnt, highlighting the key roles of pre-existing research collaborations and focal centres of coordination in contributing to the success of these initiatives. We conclude with recommendations about important ways in which the scientific research community could be better prepared for future pandemics. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

7.
J Theor Biol ; 557: 111331, 2022 Oct 26.
Article in English | MEDLINE | ID: covidwho-2086500

ABSTRACT

The emergence of SARS-CoV-2 saw severe detriments to public health being inflicted by COVID-19 disease throughout 2020. In the lead up to Christmas 2020, the UK Government sought an easement of social restrictions that would permit spending time with others over the Christmas period, whilst limiting the risk of spreading SARS-CoV-2. In November 2020, plans were published to allow individuals to socialise within 'Christmas bubbles' with friends and family. This policy involved a planned easing of restrictions in England between 23-27 December 2020, with Christmas bubbles allowing people from up to three households to meet throughout the holiday period. We estimated the epidemiological impact of both this and alternative bubble strategies that allowed extending contacts beyond the immediate household. We used a stochastic individual-based model for a synthetic population of 100,000 households, with demographic and SARS-CoV-2 epidemiological characteristics comparable to England as of November 2020. We evaluated five Christmas bubble scenarios for the period 23-27 December 2020, assuming our populations of households did not have symptomatic infection present and were not in isolation as the eased social restrictions began. Assessment comprised incidence and cumulative infection metrics. We tested the sensitivity of the results to a situation where it was possible for households to be in isolation at the beginning of the Christmas bubble period and also when there was lower adherence to testing, contact tracing and isolation interventions. We found that visiting family and friends over the holiday period for a shorter duration and in smaller groups was less risky than spending the entire five days together. The increases in infection from greater amounts of social mixing disproportionately impacted the eldest. We provide this account as an illustration of a real-time contribution of modelling insights to a scientific advisory group, the Scientific Pandemic Influenza Group on Modelling, Operational sub-group (SPI-M-O) for the Scientific Advisory Group for Emergencies (SAGE) in the UK, during the COVID-19 pandemic. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

8.
Ann Oper Res ; : 1-36, 2022 Jan 24.
Article in English | MEDLINE | ID: covidwho-2085417

ABSTRACT

The impact of blockchain technology (BCT) implementation on the accuracy, reliability, visibility, incorruptibility, and timeliness of supply-chain processes and transactions, makes it attractive to improve the robustness, transparency, accountability and decision-making in risk management. Therefore, the emerging BCT can present an invaluable opportunity for the organisations in need of preparing for and responding to uncertain and complex instances. The adoption of BCT in the operations and supply chain management (OSCM) literature remains scarcely investigated, especially in the context of managing risks in emergency situations such as crises, disasters, and pandemics, which are characterised by volatility, uncertainty, complexity and ambiguity (VUCA) in the business environment. This article will contribute to the OSCM literature by developing a conceptual model that will examine the causal relationships between VUCA business environment, constructs derived from technology acceptance model (TAM), resilience and behavioural intention of the operations managers to adopt BCT for risk management. The model was tested by gathering responses from 116 operations managers in the UK (during COVID-19 pandemic) through structural equation modelling. Findings from the analysis suggest that understanding the benefits of BCT, involvement in resilient organisational practices and user-friendly implementation of the technology will have a significant and positive influence on the intention to adopt BCT for risk management in the OSCM context. Building upon these findings, we have proposed a BCT decision framework to assess the feasibility and suitability of adopting BCT in each context (such as risk management), which will have strategic implications for operations managers and the OSCM community.

9.
Museum Management and Curatorship ; JOUR: 1-18,
Article in English | Web of Science | ID: covidwho-2082421

ABSTRACT

This paper recommends the marketing mix modelling methodology for improving the marketing and exhibition activities at museums and art galleries as it can complement other currently used methods such as surveys or qualitative research. Focus is placed on the Zacheta - National Gallery of Art. Econometric modelling is used to quantify the impact of independent factors (e.g., weather, holidays, events and COVID-19 pandemic) on the popularity of the gallery. The impact of the media and individual exhibition types are analysed in relation to visitor interest. Recommendations for the planning of future activities are made.

10.
Curr Opin Environ Sci Health ; : 100399, 2022 Oct 22.
Article in English | MEDLINE | ID: covidwho-2082634

ABSTRACT

Contagious diseases are needed to be monitored to prevent spreading within communities. Timely advice and predictions are necessary to overcome the consequences of those epidemics. Currently, emphasis has been placed on computer modelling to achieve the needed forecasts, the best example being the COVID-19 pandemic. Scientists used various models to determine how diverse sociodemographic factors correlated and influenced COVID-19 Global transmission and demonstrated the utility of computer models as tools in disease management. However, as modelling is done with assumptions with set rules, calculating uncertainty quantification is essential in infectious modelling when reporting the results and trustfully describing the limitations. This article summarizes the infectious disease modelling strategies, challenges, and global applicability by focusing on the COVID-19 pandemic.

11.
R Soc Open Sci ; 9(10): 220064, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-2078024

ABSTRACT

We present a simple model for the spread of an infection that incorporates spatial variability in population density. Starting from first-principle considerations, we explore how a novel partial differential equation with state-dependent diffusion can be obtained. This model exhibits higher infection rates in the areas of higher population density-a feature that we argue to be consistent with epidemiological observations. The model also exhibits an infection wave, the speed of which varies with population density. In addition, we demonstrate the possibility that an infection can 'jump' (i.e. tunnel) across areas of low population density towards areas of high population density. We briefly touch upon the data reported for coronavirus spread in the Canadian province of Nova Scotia as a case example with a number of qualitatively similar features as our model. Lastly, we propose a number of generalizations of the model towards future studies.

12.
Geotechnical Engineering ; 53(3):1-6, 2022.
Article in English | Scopus | ID: covidwho-2073566

ABSTRACT

The design and construction of tunnels is among the most specialized fields in underground engineering. There are various uncertainties during tunnel excavation. Predictable and unpredictable uncertainties are important sources of risk in tunnel engineering. The effect of uncertainty on risk assessment and decision-making is therefore provided priority, particularly for tunnel projects where predictable uncertainty is often the primary cause of risk. First phase of the Kabatas-Mahmutbey tunnel excavation, some collapses occurred due to uncertainties in different parts of the tunnel route. Regardless, Kabatas-Mahmutbey metro line, which is 24.5 km long and consists of 19 stations, was planned to be operational in the first quarter of 2020. For this purpose, electrical and mechanical tests were successfully continuing within the scope of the project. In addition, the signalling works of the metro line were near to the end. All technical uncertainties and risks were thought to be circumvented. In the last phase of the project, Coronavirus Disease 2019 (COVID-19) was encountered as unpredictable uncertainty. Unfortunately, due to COVID-19, the work in the project has been postponed to a later date. With the commissioning of the metro project, which will breathe the traffic of Istanbul upon commissioning, it is foreseen to carry 500 thousand passengers a day. © 2022, Southeast Asian Geotechnical Society. All rights reserved.

13.
Journal of Health Management ; 2022.
Article in English | Web of Science | ID: covidwho-2070670

ABSTRACT

The aim of this research is to uncover whether nurses' fear of contracting Coronavirus Disease 2019 (COVID-19) has resulted in stress-related presenteeism and burnout, and whether perceived organisational support is effective in dealing with both nurses' fear of contracting COVID-19 and its undesired consequences. For this purpose, a cross-sectional and descriptive research has been conducted. The data are collected from 513 nurses working in Ankara, Turkey, through a questionnaire survey. Independent samples t-test, one-way analysis of variance test and partial least squares structural equation modelling technique are employed to analyse the data. Findings indicate that nurses fear infection and experience stress-related presenteeism and burnout considerably. However, they perceive slightly inadequate level of organisational support. Fear of infection has resulted in stress-related presenteeism and burnout. Stress-related presenteeism has mediated the relationship between fear of infection and burnout. Perceived organisational support has negatively related to fear of infection and its negative consequences. In this research, to our knowledge, for the first time, the burnout, stress-related presenteeism, fear of infection and perceived organisational support levels of nurses are compared according to the pandemic-related criteria. Besides, the mediating role of nurses' stress-related presenteeism between their fear of contracting COVID-19 and burnout is discovered.

14.
Review of International Economics ; 2022.
Article in English | Web of Science | ID: covidwho-2070532

ABSTRACT

This article employs gravity modeling to examine the effect of COVID-19 on global and intra-commonwealth trade. It uses bilateral monthly exports, number of COVID-19 cases and deaths and the stringency of measures. The main novelty is the use of price indices as proxies for multilateral resistance terms, which allow us to identify, supply, and demand effects of Covid-19 on bilateral trade. The incidence of COVID-19 impacts commonwealth trade flows, the effect varies with the development level. High numbers of COVID-19 cases, including deaths, in low-income importers reduced commonwealth exports unlike high-income importers that show higher exports. The incidence of COVID in an exporters' neighbouring countries impacted trade and restrictions in high-income countries increased commonwealth trade. Short-term trends project a negative change in both exports and imports of commonwealth countries.

15.
Heliyon ; 8(10): e11065, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-2069053

ABSTRACT

Today, online consumers' shopping experiences are wholly transformed because of technology to maximize consumer shopping experiences. However, even after increased online shopping after the Covid-19 outbreak, no study has examined the role of demographics in online shopping acceptance. Thus, we filled this gap by employing a cross-sectional design in the UAE and conducting Structural Equation Modelling (SEM). For data gathering purposes, we used structured questionnaires and randomly selected a sample of n = 320 respondents from Al Ain city. Findings revealed strong relationships between Online Shopping, Social Media Usage, and Electronic Word of Mouth (p > 0.000, p > 0.000). Despite the relationships between Social Media Usage, Electronic Word of Mouth, and online shopping acceptance remaining insignificant (p < .384, p < .425), the relationship between Social Media Usage, Online Shopping Acceptance (p > .004) remained significant. Finally, we conducted the mediating analyses and found a substantial mediation of gender between Social Media Usage, Online Shopping Acceptance (p > .000), and Electronic Word of Mouth and Online Shopping Acceptance (p >. 001). Hence, we conclude that people from Al-Ain city primarily rely on online shopping. For this purpose, they consider different factors, including their demographics, i.e., gender, as highly influential on their online shopping acceptance. However, the major limitations of the current study involve selecting gender as the only mediating variable, rejection o two prominent hypotheses, and geographical generalizability of results. Finally, we recommend that future researchers examine the impact of other demographical variables, i.e., age, income, qualification, residence, and others, to examine their impacts on consumer online shopping acceptance.

16.
Journal of Applied Econometrics ; 2022.
Article in English | Web of Science | ID: covidwho-2068573

ABSTRACT

Adrian, Boyarchenko and Giannone ((2019), ABG) adapt quantile regression (QR) methods to examine the relationship between US economic growth and financial conditions. We confirm their empirical findings, using their methodology and their pre-2016 sample. Mindful of the importance of the Covid-19 pandemic, we extend the sample to 2021Q3 and find attenuation of the key estimated coefficients using ABG's empirical methods. Given the pandemic observations, we provide robust QR analysis of dependence based on ranked data and explain the relationship with extant copula modelling methods.

17.
2022 European Control Conference, ECC 2022 ; : 743-748, 2022.
Article in English | Scopus | ID: covidwho-2013200

ABSTRACT

We propose a model predictive control (MPC) approach for minimising the social distancing and quarantine measures during a pandemic while maintaining a hard infection cap. To this end, we study the admissible and the maximal robust positively invariant set (MRPI) of the standard SEIR compartmental model with control inputs. Exploiting the fact that in the MRPI all restrictions can be lifted without violating the infection cap, we choose a suitable subset of the MRPI to define terminal constraints in our MPC routine and show that the number of infected people decays exponentially within this set. Furthermore, under mild assumptions we prove existence of a uniform bound on the time required to reach this terminal region (without violating the infection cap) starting in the admissible set. The findings are substantiated based on a numerical case study. © 2022 EUCA.

18.
Mathematical Biosciences and Engineering ; 19(12):13861-13877, 2022.
Article in English | Scopus | ID: covidwho-2066722

ABSTRACT

The ongoing COVID-19 pandemic has created major public health and socio-economic challenges across the United States. Among them are challenges to the educational system where college administrators are struggling with the questions of how to mitigate the risk and spread of diseases on their college campus. To help address this challenge, we developed a flexible computational framework to model the spread and control of COVID-19 on a residential college campus. The modeling framework accounts for heterogeneity in social interactions, activities, environmental and behavioral risk factors, disease progression, and control interventions. The contribution of mitigation strategies to disease transmission was explored without and with interventions such as vaccination, quarantine of symptomatic cases, and testing. We show that even with high vaccination coverage (90%) college campuses may still experience sizable outbreaks. The size of the outbreaks varies with the underlying environmental and socio-behavioral risk factors. Complementing vaccination with quarantine and mass testing was shown to be paramount for preventing or mitigating outbreaks. Though our quantitative results are likely provisional on our model assumptions, sensitivity analysis confirms the robustness of their qualitative nature. ©2022 the Author(s)

19.
Sustainability ; 14(19):12837, 2022.
Article in English | ProQuest Central | ID: covidwho-2066469

ABSTRACT

This manuscript proposes an integrated system for treating hospital solid waste (H.S.W.) consisting of an incineration and frictional sterilization system capable of operating during normal and emergency situations. We analyzed the benefits of integrating different hospital solid waste (H.S.W.) treatment systems with the existing stand-alone incineration system, with a particular emphasis on the thermal friction sterilization integration system. The objective was to define the economic advantages and benefits in terms of resources recovery of using the thermal frictional sterilization–incineration integrated system during the hospital’s normal and emergency/pandemic operating conditions. We modeled three modeling scenarios based on normal and emergency operating conditions. The results show that the H.S.W. was composed of 74% general H.S.W. Existing incineration systems would be the most expensive process because the sanitary transportation cost represented approximately 96% of the H.S.W. costs. The hospital would realize 40–61% savings relative to the existing method if the integrated incineration–frictional systems were implemented to treat 50–70% of H.S.W.;the savings were better than in other scenarios. Proposed scenario 3 had a much better resources recovery factor than scenarios 1 and 2. This modeling study showed that a thermal frictional sterilization–incineration system could work well even under emergency conditions if the H.S.W. in-house sorting/transportation/storage process is modified to cater to other H.S.W. treatment/sterilization systems.

20.
Complexity ; 2022, 2022.
Article in English | ProQuest Central | ID: covidwho-2064333

ABSTRACT

We propose a theoretical study to investigate the spread of the SARS-CoV-2 virus, reported in Wuhan, China. We develop a mathematical model based on the characteristic of the disease and then use fractional calculus to fractionalize it. We use the Caputo-Fabrizio operator for this purpose. We prove that the considered model has positive and bounded solutions. We calculate the threshold quantity of the proposed model and discuss its sensitivity analysis to find the role of every epidemic parameter and the relative impact on disease transmission. The threshold quantity (reproductive number) is used to discuss the steady states of the proposed model and to find that the proposed epidemic model is stable asymptotically under some constraints. Both the global and local properties of the proposed model will be performed with the help of the mean value theorem, Barbalat’s lemma, and linearization. To support our analytical findings, we draw some numerical simulations to verify with graphical representations.

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